AI applications in forest monitoring need remote sensing benchmark datasets. - Emily R. Lines; Matt Allen 001; Carlos Cabo; Kim Calders; Amandine Debus; Stuart W. D. Grieve; Milto Miltiadou; Adam Noach; Harry J. F. Owen; Stefano Puliti (IEEE Big Data, 2022)

Fewshot learning on global multimodal embeddings for earth observation tasks. - Matt Allen 001; Francisco Dorr; Joseph A. Gallego-Mejia; Laura Martínez-Ferrer; Anna Jungbluth; Freddie Kalaitzis; Raúl Ramos-Pollán (CoRR, 2023)

Large Scale Masked Autoencoding for Reducing Label Requirements on SAR Data. - Matt Allen 001; Francisco Dorr; Joseph A. Gallego-Mejia; Laura Martínez-Ferrer; Anna Jungbluth; Freddie Kalaitzis; Raúl Ramos-Pollán (CoRR, 2023)

Exploring Generalisability of Self-Distillation with No Labels for SAR-Based Vegetation Prediction. - Laura Martínez-Ferrer; Anna Jungbluth; Joseph A. Gallego-Mejia; Matt Allen 001; Francisco Dorr; Freddie Kalaitzis; Raúl Ramos-Pollán (CoRR, 2023)

Exploring DINO: Emergent Properties and Limitations for Synthetic Aperture Radar Imagery. - Joseph A. Gallego-Mejia; Anna Jungbluth; Laura Martínez-Ferrer; Matt Allen 001; Francisco Dorr; Freddie Kalaitzis; Raúl Ramos-Pollán (CoRR, 2023)

M3LEO: A Multi-Modal, Multi-Label Earth Observation Dataset Integrating Interferometric SAR and Multispectral Data. - Matthew J. Allen 001; Francisco Dorr; Joseph Alejandro Gallego-Mejia; Laura Martínez-Ferrer; Anna Jungbluth; Freddie Kalaitzis; Raúl Ramos-Pollán (NeurIPS, 2024)

M3LEO: A Multi-Modal, Multi-Label Earth Observation Dataset Integrating Interferometric SAR and RGB Data. - Matthew J. Allen 001; Francisco Dorr; Joseph Alejandro Gallego-Mejia; Laura Martínez-Ferrer; Anna Jungbluth; Freddie Kalaitzis; Raúl Ramos-Pollán (CoRR, 2024)

Benchmarking tree species classification from proximally-sensed laser scanning data: introducing the FOR-species20K dataset. - Stefano Puliti; Emily R. Lines; Jana Müllerová; Julian Frey; Zoe Schindler; Adrian Straker; Matt Allen 001; Lukas Winiwarter; Nataliia Rehush; Hristina Hristova; Brent Murray; Kim Calders; et al. (CoRR, 2024)

Low-Cost Tree Crown Dieback Estimation Using Deep Learning-Based Segmentation. - Matthew J. Allen 001; D. Moreno-Fernández; Paloma Ruiz-Benito; Stuart W. D. Grieve; Emily R. Lines (CoRR, 2024)

PointsToWood: A deep learning framework for complete canopy leaf-wood segmentation of TLS data across diverse European forests. - Harry J. F. Owen; Matthew J. Allen 001; Stuart W. D. Grieve; Phill Wilkes; Emily R. Lines (CoRR, 2025)

Manual Labelling Artificially Inflates Deep Learning-Based Segmentation Performance on RGB Images of Closed Canopy: Validation Using TLS. - Matthew J. Allen 001; Harry J. F. Owen; Stuart W. D. Grieve; Emily R. Lines (CoRR, 2025)